AI

Conclusion: Machine learning operations (MLOps) adapts principles, practices and measures from developer operations (DevOps), but significantly transforms some aspects to address the different skill sets and quality control challenges and deployment nuances of machine learning (ML) and data engineering.

Implementing MLOps has several benefits, from easing collaboration among project team members to reducing bias in the resulting artificial intelligence (AI) models.

Conclusion: While the current artificial intelligence (AI) initiatives are data-driven, there are instances whereby the current data is insufficient to predict the future. For example, answering the following questions might be challenging if the available data is only of a historical nature irrelevant for forecasting purposes:

  • Q1: What will be the effect on sales if the price is increased by 10 % as of the next quarter?
  • Q2: What would have happened to sales had we increased the price by 10 % six months ago?

The purpose of this note is to provide a framework that can be used to derive sales principles to answer the above questions. The same approach can be used to derive other business processes principles such as procurement, customer service and client complaints tracking.

Related Articles:

"Acknowledging the limits of machine learning during AI-enabled transformation" IBRS, 2019-01-06 22:29:52

"Analytics artificial intelligence maturity model" IBRS, 2018-12-03 09:44:43

"Machine learning will displace “extract, transform and load” in business intelligence and data integration" IBRS, 2018-02-01 10:03:37

 

Conclusion: Artificial intelligence technologies are available in various places such as robotic process automation (RPA), virtual agents and analytics. The purpose of this paper is to provide an AI maturity model in the analytics space. The proposed maturity model can be applied to any type of industry. It provides a roadmap to help improve business performance in the following areas:

  • Running the business (RTB): Provide executives with sufficient information to make informed decisions about running the business and staying competitive.
  • Growing the business (GTB): Provides information about growing the business in various geographies without changing the current services and products.
  • Transforming the business (TTB): Provides information to develop and release new products and services ahead of competitors.

Many IT organisations are trying to change their perceived image from high-cost / low quality to value-added service providers. However, many of the adopted approaches revolve around improving just few processes (e.g. problem management). While these processes are important, they are insufficient to produce the desired effect for IT groups to deliver value-added services. 

In this IBRS Master Advisory Presentation (MAP), IBRS outlines the high-level issues, surrounding Running IT as a Service from both business and technology viewpoints.This MAP is designed to guide and stimulate discussions between business and technology groups and point the way for more detailed activity. It also provides links to further reading to support these follow-up activities.

The MAP is provided as a set of presentation slides,  and as a script and executive briefing document.